Facilitators and barriers to accessing reproductive health care for migrant beer promoters in Cambodia, Laos, Thailand and Vietnam: A mixed methods study
Bibliographic record
Abstract
BACKGROUND: The purpose of the research was to assess access to sexual and reproductive health services for migrant women who work as beer promoters. This mixed methods research was conducted in Phnom Penh, Cambodia, Bangkok, Thailand, Vientiane, Laos, and Hanoi, Vietnam during 2010 to 2011. METHODS: Focus groups were held with beer promoters and separate focus groups or interviews with key informants to explore the factors affecting beer promoters' access to health care institutions for reproductive health care. The findings of the focus groups were used to develop a survey for beer promoters. This survey was conducted in popular health institutions for these women in each of the four Asian cities. RESULTS: Several common themes were evident. Work demands prevented beer promoters from accessing health care. Institutional factors affecting care included cost, location, environmental factors (e.g. waiting times, cleanliness and confidentiality) and service factors (e.g. staff attitudes, clinic hours, and availability of medications). Personal factors affecting access were shyness and fear, lack of knowledge, and support from family and friends.The survey of the beer promoters confirmed that cost, location and both environmental and service factors impact on access to health care services for beer promoters. Many beer promoters are sexually active, and a significant proportion of those surveyed rely on sex work to supplement their income. Many also drink with their clients. Despite a few differences amongst the surveyed population, the findings were remarkably similar across the four research sites. CONCLUSIONS: Recommendations from the research include the provision of evening and weekend clinic hours to facilitate access, free or low cost clinics, and health insurance through employer or government plans which are easy to access for migrants. Other improvements that would facilitate the access of beer promoters to these services include increased funding to hire more staff (reducing waiting times) and to stock more needed medications, mobile clinics to come to the workplace or free transportation for beer promoters to the clinics, improved training to reduce health care provider stigma against beer promoters, and public education about the importance of reproductive health care, including preventative services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".